MARVIS system uses VLM reasoning over visualizations for predictive tasks

Researchers have developed MARVIS, a novel system that enhances the reasoning capabilities of large language and vision-language models (VLMs) by converting their latent embeddings into visual representations. This approach allows VLMs to interpret these visualizations, leading to improved predictive performance across diverse domains including vision, audio, biology, and tabular data. A single 3 billion parameter MARVIS model demonstrated competitive results, outperforming Google's Gemini 2.0 by an average of 16% without requiring domain-specific training. AI

影响 Enhances VLM reasoning by visualizing embeddings, potentially improving performance on diverse data types without domain-specific tuning.

排序理由 Academic paper detailing a new system for improving VLM reasoning capabilities.

在 arXiv cs.LG 阅读 →

• arXiv
• Gemini 2.0
• Google
• LLM
• MARVIS
• VLM

• 论文
• 模型发布

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